From 859e465da7ecf25c880cd509e911044fe188a019 Mon Sep 17 00:00:00 2001
From: letiziaia <45148109+letiziaia@users.noreply.github.com>
Date: Fri, 8 Mar 2024 22:36:02 +0200
Subject: [PATCH] Refactor/rename mutualcl (#3)

* refactor

* fix typing

* update tests after refactoring

* add mypy

* add stubs

* refactor: naming and types

* default lint config

* unused import

* typing and linting

* untyped import

* revert to same pipfile

* fix
---
 .github/workflows/validate.yml                |    1 +
 Pipfile                                       |   21 +-
 Pipfile.lock                                  | 2495 -----------------
 README.md                                     |    5 +-
 RUNBOOK.md                                    |   30 +-
 multilayer_alignment/alignment_score.py       |   67 +-
 multilayer_alignment/consensus.py             |  126 +
 multilayer_alignment/mutual_clusters.py       |  121 -
 multilayer_alignment/null_models.py           |   44 +-
 multilayer_alignment/visualizations.py        |   72 +-
 ruff.toml                                     |   77 +
 setup.py                                      |    2 +-
 tests/test_compute_maximal_alignment_curve.py |    6 +-
 ...test_compute_multilayer_alignment_score.py |    4 +-
 ...labels.py => test_get_consensus_labels.py} |   24 +-
 ...ers.py => test_get_consensus_partition.py} |  100 +-
 ...test_get_consensus_partition_recursive.py} |  100 +-
 17 files changed, 442 insertions(+), 2853 deletions(-)
 delete mode 100644 Pipfile.lock
 create mode 100644 multilayer_alignment/consensus.py
 delete mode 100644 multilayer_alignment/mutual_clusters.py
 create mode 100644 ruff.toml
 rename tests/{test_get_mutual_clusters_labels.py => test_get_consensus_labels.py} (51%)
 rename tests/{test_compute_mutual_clusters.py => test_get_consensus_partition.py} (68%)
 rename tests/{test_compute_mutual_clusters_recursive.py => test_get_consensus_partition_recursive.py} (68%)

diff --git a/.github/workflows/validate.yml b/.github/workflows/validate.yml
index 4444767..b3eb781 100644
--- a/.github/workflows/validate.yml
+++ b/.github/workflows/validate.yml
@@ -22,6 +22,7 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           pip install pipenv
+          pipenv lock
           pipenv verify
           pipenv install --dev
       - name: Validate
diff --git a/Pipfile b/Pipfile
index 2f426fe..0216de1 100644
--- a/Pipfile
+++ b/Pipfile
@@ -4,21 +4,17 @@ verify_ssl = true
 name = "pypi"
 
 [packages]
-click = ">=8.0"
-clusim = "*"
-cython = "*"
+click = "*"
 joblib = "~=1.2.0"
-loguru = "~=0.6.0"
+loguru = "*"
 matplotlib = "~=3.6.1"
 networkx = "*"
-numba = "*"
-numpy = "~=1.24.0"
+numpy = "*"
 pandas = "==1.3.5"
-scikit-learn = "==1.0.2"
+scikit-learn = "~=1.4.1"
 scipy = "*"
 seaborn = "*"
 setuptools = "*"
-regex = "*"
 tqdm = "*"
 wheel = "*"
 
@@ -26,12 +22,15 @@ wheel = "*"
 black = "*"
 coverage = "*"
 flake8 = "*"
+mypy = "*"
 notebook = "*"
-pip-audit = ">=2.4.10"
+pandas-stubs = "*"
+pip-audit = "*"
 ruff = "*"
+types-tqdm = "*"
 
 [scripts]
-validate = "bash -c 'python3 -m flake8 && python3 -m pip_audit'"
+validate = "bash -c 'python3 -m flake8 && python3 -m mypy . && python3 -m pip_audit'"
 
 [requires]
-python_version = "3.10"
+
diff --git a/Pipfile.lock b/Pipfile.lock
deleted file mode 100644
index 1ad152b..0000000
--- a/Pipfile.lock
+++ /dev/null
@@ -1,2495 +0,0 @@
-{
-    "_meta": {
-        "hash": {
-            "sha256": "c9bf231b4f294caece7297bc67a40768be5fc5fe8c0e2d4019073c7c5da2224d"
-        },
-        "pipfile-spec": 6,
-        "requires": {
-            "python_version": "3.10"
-        },
-        "sources": [
-            {
-                "name": "pypi",
-                "url": "https://pypi.org/simple",
-                "verify_ssl": true
-            }
-        ]
-    },
-    "default": {
-        "click": {
-            "hashes": [
-                "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28",
-                "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de"
-            ],
-            "index": "pypi",
-            "version": "==8.1.7"
-        },
-        "clusim": {
-            "hashes": [
-                "sha256:f2fc70ec9238610408072604a235b1598f02f3cca28b4bc4a3a6ec25024affa2"
-            ],
-            "index": "pypi",
-            "version": "==0.4"
-        },
-        "colorama": {
-            "hashes": [
-                "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44",
-                "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"
-            ],
-            "markers": "platform_system == 'Windows'",
-            "version": "==0.4.6"
-        },
-        "contourpy": {
-            "hashes": [
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-                "sha256:11f8d2554e52f459918f7b8e6aa20ec2a3bce35ce95c1f0ef4ba36fbda306df5",
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diff --git a/README.md b/README.md
index 5c1f75c..314035b 100644
--- a/README.md
+++ b/README.md
@@ -5,15 +5,14 @@
 
 # multilayer-alignment
 
-This repository implements an algorithm for quantifying multilayer alignment.
-You can refer to the [slide deck](https://docs.google.com/presentation/d/1HMEE5kOwwJPLBmAgycKIMSWRx0eCxd3RtSxVR1Jdczw/) for the general idea.
+This repository implements an algorithm for quantifying multilayer alignment or higher-order alignment, that is, the alignment across n different dimensions. You can refer to the [slide deck](https://docs.google.com/presentation/d/1HMEE5kOwwJPLBmAgycKIMSWRx0eCxd3RtSxVR1Jdczw/) for the original idea.
 
 ## Structure of the repo
 
 - `\multilayer_alignment\`: source code
 - `\tests\`: tests for the source code
 
-## Setting up
+## Setting up the development environment
 
 ![python](https://img.shields.io/badge/Python-FFD43B?style=for-the-badge&logo=python&logoColor=blue)
 
diff --git a/RUNBOOK.md b/RUNBOOK.md
index ff04b6b..631f246 100644
--- a/RUNBOOK.md
+++ b/RUNBOOK.md
@@ -2,10 +2,10 @@
 
 ## Overview
 
-This repository contains code to extend pairwise measure of alignment
-to N-wise case.
+This repository contains code to extend a pairwise measure of alignment
+based on mutual information to an N-wise case.
 
-## Operational tasks
+## Development tasks
 
 ### Install all dependencies and activate the environment
 
@@ -25,26 +25,26 @@ From root directory,
 $ python -m unittest discover -v
 ```
 
-### Given partitions for each of the layers, compute mutual clusters
+### Given opinion partitions for each of the topics, compute the consensus partition
 
 ```python
 # import needed modules
 >>> import pandas as pd
->>> from multilayer_alignment.mutual_clusters import compute_mutual_clusters_recursive
+>>> from multilayer_alignment.consensus import get_consensus_partition
 
-# load the partitions labels to a pandas DataFrame
+# load the opinion labels to a pandas DataFrame
 >>> df = pd.DataFrame(
     {
-        # in layer A, nodes 0 and 1 have label 0,
-        # nodes 2 and 3 have label 1
+        # on topic A, individuals 0 and 1 have opinion 0,
+        # individuals 2 and 3 have opinion 1
         "A": [0, 0, 1, 1],
         "B": [0, 1, 0, 1],
         "C": [1, 0, 1, 0]
     }
 )
 
-# get mutual clusters
->>> compute_mutual_clusters_recursive(cluster_labels_df=df)
+# get consensus partition
+>>> get_consensus_partition(opinions=df)
 {
     "A0_B0_C1": {0},
     "A0_B1_C0": {1},
@@ -58,21 +58,21 @@ Alternatively:
 ```python
 # import needed modules
 >>> import pandas as pd
->>> from multilayer_alignment.mutual_clusters import compute_mutual_clusters
+>>> from multilayer_alignment.consensus import get_consensus_partition_recursive
 
 # load the partitions labels to a pandas DataFrame
 >>> df = pd.DataFrame(
     {
-        # in layer A, nodes 0 and 1 have label 0,
-        # nodes 2 and 3 have label 1
+        # on topic A, individuals 0 and 1 have opinion 0,
+        # individuals 2 and 3 have opinion 1
         "A": [0, 0, 1, 1],
         "B": [0, 1, 0, 1],
         "C": [1, 0, 1, 0]
     }
 )
 
-# get mutual clusters
->>> compute_mutual_clusters(cluster_labels_df=df)
+# get consensus partition
+>>> get_consensus_partition_recursive(opinions=df)
 {
     "A0_B0_C1": {0},
     "A0_B1_C0": {1},
diff --git a/multilayer_alignment/alignment_score.py b/multilayer_alignment/alignment_score.py
index dbc546a..b94b6da 100644
--- a/multilayer_alignment/alignment_score.py
+++ b/multilayer_alignment/alignment_score.py
@@ -3,23 +3,23 @@
 import numpy as np
 from itertools import combinations
 from functools import partial
-from joblib import dump
+from joblib import dump  # type: ignore
 
-from sklearn.metrics.cluster import normalized_mutual_info_score
-from sklearn.metrics import adjusted_mutual_info_score
+from sklearn.metrics.cluster import normalized_mutual_info_score  # type: ignore
+from sklearn.metrics import adjusted_mutual_info_score  # type: ignore
 
 import multiprocessing as mp
 from multiprocessing.pool import Pool
 from tqdm import tqdm
 
-from multilayer_alignment.mutual_clusters import compute_mutual_clusters
-from multilayer_alignment.mutual_clusters import get_mutual_clusters_labels
+from multilayer_alignment.consensus import get_consensus_partition
+from multilayer_alignment.consensus import get_consensus_labels
 
 from multilayer_alignment.utils.logging import logger
 
 
 def _compute_layer_expectation(
-    layer: np.array, scoring_function: typing.Callable
+    layer: typing.Iterable, scoring_function: typing.Callable
 ) -> float:
     """
     :param layer: 1d np.array with clustering assignment
@@ -29,22 +29,23 @@ def _compute_layer_expectation(
     _all_scores = []
     with Pool(processes=mp.cpu_count() - 1) as pool:
         result = pool.map_async(
-            scoring_function, [np.random.permutation(layer) for _ in range(10)]
-        )
+            scoring_function, [np.random.permutation(layer) for _ in range(10)]  # type: ignore
+        )  # type: ignore
         for value in result.get():
-            # NOTE: assuming scores are always >=0
+            # NOTE: in case of AMI, it is possible to get negative scores,
+            # but we cap them to 0 so get only scores >= 0
             _all_scores.append(max(value, 0))
     return np.array(_all_scores).mean()
 
 
-def compute_multilayer_alignment_score(
-    cluster_labels_df: pd.DataFrame,
+def multilayer_alignment_score(
+    opinions: typing.Union[pd.DataFrame, pd.Series],
     mutual_clusters_labels: typing.List,
     which_score: str = "nmi",
     adjusted: bool = False,
 ) -> float:
     """
-    :param cluster_labels_df: pd.DataFrame having one column per layer and one row per node,
+    :param opinions: pd.DataFrame having one column per layer and one row per node,
         where each element a_ij is an integer representing the cluster labels for node i at layer j
     :param mutual_clusters_labels: list, a list of labels for mutual clusters
     :param which_score: str, one of "nmi" or "ami"
@@ -59,9 +60,9 @@ def compute_multilayer_alignment_score(
         _score_f = adjusted_mutual_info_score
 
     avg_nmi = 0
-    _expected_nmi = 0
-    for layer_id in cluster_labels_df.columns:
-        _layer = cluster_labels_df[layer_id].values
+    _expected_nmi = 0.0
+    for layer_id in opinions.columns:
+        _layer = opinions[layer_id].values
         _score = _score_f(_layer, mutual_clusters_labels, average_method="arithmetic")
         avg_nmi += _score
 
@@ -72,17 +73,17 @@ def compute_multilayer_alignment_score(
                     _score_f, mutual_clusters_labels, **{"average_method": "arithmetic"}
                 ),
             )
-    return (avg_nmi - _expected_nmi) / len(cluster_labels_df.columns)
+    return (avg_nmi - _expected_nmi) / len(opinions.columns)
 
 
-def compute_maximal_alignment_curve(
-    cluster_labels_df: pd.DataFrame,
+def maximal_alignment_curve(
+    opinions: typing.Union[pd.DataFrame, pd.Series],
     which_score: str = "nmi",
     adjusted: bool = False,
     dump_to: typing.Optional[str] = None,
 ) -> typing.Tuple:
     """
-    :param cluster_labels_df: pd.DataFrame having one column per layer and one row per node,
+    :param opinions: pd.DataFrame having one column per layer and one row per node,
         where each element a_ij is an integer representing the cluster labels for node i at layer j
     :param which_score: str, one of "nmi" or "ami"
     :param adjusted: bool, default: False
@@ -103,39 +104,39 @@ def compute_maximal_alignment_curve(
 
     best_by_combination_size = dict()
     all_scores_by_combination_size = dict()
-    _num_of_layers = len(cluster_labels_df.columns)
+    _num_of_layers = len(opinions.columns)
     # skipping size 1
     for length in range(2, _num_of_layers + 1):
         logger.info(f"combinations of size {length}")
-        # Get all combinations of cluster_labels_df.columns of length "length"
-        _columns_combinations = combinations(cluster_labels_df.columns, length)
+        # Get all combinations of opinions.columns of length "length"
+        _columns_combinations = combinations(opinions.columns, length)
 
         best_layers_combination = None
-        best_nmi = 0
+        best_nmi = 0.0
         # best_layers_combination_mutual_communities = dict()
 
-        for l_comb in tqdm(_columns_combinations):
-            l_comb = list(l_comb)
-            l_comb_df = cluster_labels_df[l_comb].copy()
+        for _l_comb in tqdm(_columns_combinations):
+            l_comb = list(_l_comb)
+            l_comb_df = opinions[l_comb].copy()
             # keep only items that have labels for all items in l_comb and reindex
             l_comb_df.dropna(inplace=True)
             l_comb_df.reset_index(drop=True, inplace=True)
-            mutual_clusters = compute_mutual_clusters(l_comb_df)
-            mutual_clusters_labels = get_mutual_clusters_labels(mutual_clusters)
+            mutual_clusters = get_consensus_partition(l_comb_df)
+            mutual_clusters_labels = get_consensus_labels(mutual_clusters)
             labels_list = (
                 mutual_clusters_labels.set_index("id")
                 .iloc[l_comb_df.index]["label"]
-                .values
+                .to_list()
             )
 
             # CRITERIA
-            nmi = compute_multilayer_alignment_score(
+            nmi = multilayer_alignment_score(
                 l_comb_df, labels_list, which_score=which_score, adjusted=adjusted
             )
 
-            all_scores_by_combination_size[
-                f"{length}+" + "+".join(sorted(l_comb))
-            ] = nmi
+            all_scores_by_combination_size[f"{length}+" + "+".join(sorted(l_comb))] = (
+                nmi
+            )
             # (
             # nmi,
             # mutual_clusters,
diff --git a/multilayer_alignment/consensus.py b/multilayer_alignment/consensus.py
new file mode 100644
index 0000000..ebd2a4f
--- /dev/null
+++ b/multilayer_alignment/consensus.py
@@ -0,0 +1,126 @@
+import pandas as pd
+from typing import Any, Dict, Set, Union
+
+
+def get_consensus_labels(consensus_partition: Dict[str, Set[Any]]) -> pd.DataFrame:
+    """
+    :param consensus_partition: a dictionary of consensus group label (str) -> consesus group members (set)
+    :return: pd.DataFrame with column 'id' for the element id and column 'label' for the element label
+    """
+    nodes_id = []
+    labels = []
+    for k, v in consensus_partition.items():
+        for elm in v:
+            nodes_id.append(elm)
+            labels.append(k)
+    return pd.DataFrame({"id": nodes_id, "label": labels}).sort_values(by="id")
+
+
+def get_consensus_partition(
+    opinions: Union[pd.DataFrame, pd.Series]
+) -> Dict[str, Set[Any]]:
+    """
+    Returns the consensus groups (faster)
+    :param opinions: pd.DataFrame having one column per topic and one row per individual,
+        where each element a_ij represents the opinion for individual i on topic j
+        and columns names are the topic names
+    :return: dict[str, set], a dictionary of consensus group label (str) -> consensus group members (set)
+        Note: Only non-empty sets are returned!
+    ------------
+    Example
+    ------------
+    E.g.:
+        opinions:               A | B | C
+                                ---------
+                             0  0   1   0
+                             1  0   1   0
+                             2  1   0   1
+        consensus groups:
+            A0_B0_C0 -> {}, A0_B0_C1 -> {}, A0_B1_C0 -> {0, 1}, A0_B1_C1 -> {},
+            A1_B0_C0 -> {}, A1_B0_C1 -> {2}, A1_B1_C0 -> {}, A1_B1_C1 -> {}
+    >>> df = pd.DataFrame({"A": [0, 0, 1], "B": [1, 1, 0], "C": [0, 0, 1]})
+    >>> get_consensus_partition(opinions=df)
+    """
+    consensus_groups = {}
+    _topics = list(opinions.columns)
+    _cg = opinions.groupby(by=_topics).groups
+    for key, value in _cg.items():
+        if len(_topics) == 1:
+            _formatted_key = f"{_topics[0]}{key}"
+        else:
+            _joined_key = [
+                "".join((str(col_name), str(label)))
+                for col_name, label in zip(_topics, key)  # type: ignore
+            ]  # type: ignore
+            _formatted_key = "_".join(_joined_key)
+        consensus_groups[_formatted_key] = set(value)
+    return consensus_groups
+
+
+def get_consensus_partition_recursive(
+    opinions: Union[pd.DataFrame, pd.Series],
+    consensus_groups: Dict[str, Set[Any]] = {},
+    next_topic_idx: int = 0,
+) -> Dict[str, Set[Any]]:
+    """
+    Recursive function that traverses all the topics and builds the consensus groups
+    :param opinions: pd.DataFrame having one column per topic and one row per individual,
+        where each element a_ij represents the opinion of individual i on topic j
+    :param consensus_groups: dict[str, set], a dictionary of consensus group label (str) -> consensus group members (set)
+        The consensus group labels are built at each step by combining the labels of the groups that are intersected.
+        Default: empty dictionary
+    :param next_topic_idx: int, the index of next topic to consider
+        Default: 0 (first column in opinions)
+    :return: current_sets, dict[str, set], a dictionary of consensus group label (str) -> consensus group members (set)
+        Note: Only non-empty sets are returned!
+    ------------
+    Example
+    ------------
+    E.g.:
+        opinions:               A | B | C
+                                ---------
+                             0  0   1   0
+                             1  0   1   0
+                             2  1   0   1
+        consensus groups (first iteration):
+            A0_B0 -> {}, A0_B1 -> {0, 1}, A1_B0 -> {2}, A1_B1 -> {}
+        consensus groups (final iteration):
+            A0_B0_C0 -> {}, A0_B0_C1 -> {}, A0_B1_C0 -> {0, 1}, A0_B1_C1 -> {},
+            A1_B0_C0 -> {}, A1_B0_C1 -> {2}, A1_B1_C0 -> {}, A1_B1_C1 -> {}
+    >>> df = pd.DataFrame({"A": [0, 0, 1], "B": [1, 1, 0], "C": [0, 0, 1]})
+    >>> get_consensus_partition_recursive(opinions=df)
+    """
+    _num_of_layers = len(opinions.columns)
+    # recursion base case: no layer left to be processed
+    if next_topic_idx == _num_of_layers:
+        return consensus_groups
+    else:
+        _next_layer = opinions.columns[next_topic_idx]
+        _updated_mutual_clusters = {}
+        if len(consensus_groups) == 0:
+            # in this case, we need to create the keys for the dictionary from scratch
+            for _cluster_label in opinions[_next_layer].unique():
+                _cluster_content = opinions[opinions[_next_layer] == _cluster_label][
+                    _next_layer
+                ].index
+                _key = str(_next_layer) + str(_cluster_label)
+                _updated_mutual_clusters[_key] = set(_cluster_content)
+            return get_consensus_partition_recursive(
+                opinions, _updated_mutual_clusters, next_topic_idx + 1
+            )
+        else:
+            # in this case, we start from the current mutual clusters and compare with the current layer
+            _updated_mutual_clusters = {}
+            for mc_id, curr_mc in consensus_groups.items():
+                for _cluster_label in opinions[_next_layer].unique():
+                    _cluster_content = opinions[
+                        opinions[_next_layer] == _cluster_label
+                    ][_next_layer].index
+                    _new_key_suffix = str(_next_layer) + str(_cluster_label)
+                    _key = str(mc_id) + "_" + _new_key_suffix
+                    _new_set = curr_mc.intersection(set(_cluster_content))
+                    if len(_new_set) > 0:
+                        _updated_mutual_clusters[_key] = _new_set
+            return get_consensus_partition_recursive(
+                opinions, _updated_mutual_clusters, next_topic_idx + 1
+            )
diff --git a/multilayer_alignment/mutual_clusters.py b/multilayer_alignment/mutual_clusters.py
deleted file mode 100644
index fd4827d..0000000
--- a/multilayer_alignment/mutual_clusters.py
+++ /dev/null
@@ -1,121 +0,0 @@
-import pandas as pd
-
-
-def get_mutual_clusters_labels(mutual_clusters: dict) -> pd.DataFrame:
-    """
-    :param mutual_clusters: a dictionary of mutual cluster label (str) -> mutual cluster members (set)
-    :return: pd.DataFrame with column 'id' for the element id and column 'label' for the element label
-    """
-    nodes_id = []
-    labels = []
-    for k, v in mutual_clusters.items():
-        for elm in v:
-            nodes_id.append(elm)
-            labels.append(k)
-    return pd.DataFrame({"id": nodes_id, "label": labels}).sort_values(by="id")
-
-
-def compute_mutual_clusters(cluster_labels_df: pd.DataFrame) -> dict:
-    """
-    Returns the mutual clusters (faster)
-    :param cluster_labels_df: pd.DataFrame having one column per layer and one row per node,
-        where each element a_ij is an integer representing the cluster labels for node i at layer j
-        and columns names are the layers names
-    :return: current_sets, dict[str, set], a dictionary of mutual cluster label (str) -> mutual cluster members (set)
-        Note: Only non-empty sets are returned!
-    ------------
-    Example
-    ------------
-    E.g.:
-        cluster_labels_df:      A | B | C
-                                ---------
-                             0  0   1   0
-                             1  0   1   0
-                             2  1   0   1
-        mutual_clusters:
-            A0_B0_C0 -> {}, A0_B0_C1 -> {}, A0_B1_C0 -> {0, 1}, A0_B1_C1 -> {},
-            A1_B0_C0 -> {}, A1_B0_C1 -> {2}, A1_B1_C0 -> {}, A1_B1_C1 -> {}
-    >>> df = pd.DataFrame({"A": [0, 0, 1], "B": [1, 1, 0], "C": [0, 0, 1]})
-    >>> compute_mutual_clusters(cluster_labels_df=df)
-    """
-    mutual_clusters = {}
-    _layers = list(cluster_labels_df.columns)
-    _mc = cluster_labels_df.groupby(by=_layers).groups
-    for key, value in _mc.items():
-        if len(_layers) == 1:
-            _formatted_key = f"{_layers[0]}{key}"
-        else:
-            _joined_key = [
-                "".join((str(col_name), str(label)))
-                for col_name, label in zip(_layers, key)
-            ]
-            _formatted_key = "_".join(_joined_key)
-        mutual_clusters[_formatted_key] = set(value)
-    return mutual_clusters
-
-
-def compute_mutual_clusters_recursive(
-    cluster_labels_df: pd.DataFrame, mutual_clusters: dict = {}, next_layer_idx: int = 0
-) -> dict:
-    """
-    Recursive function that traverses all the layers and builds the mutual clusters
-    :param cluster_labels_df: pd.DataFrame having one column per layer and one row per node,
-        where each element a_ij is an integer representing the cluster labels for node i at layer j
-    :param mutual_clusters: dict[str, set], a dictionary of mutual cluster label (str) -> mutual cluster members (set)
-        The mutual cluster labels are built at each step by combining the labels of the clusters that are intersected.
-        Default: empty dictionary
-    :param next_layer_idx: int, the index of next layer to consider
-        Default: 0 (first column in cluster_labels_df)
-    :return: current_sets, dict[str, set], a dictionary of mutual cluster label (str) -> mutual cluster members (set)
-        Note: Only non-empty sets are returned!
-    ------------
-    Example
-    ------------
-    E.g.:
-        cluster_labels_df:      A | B | C
-                                ---------
-                             0  0   1   0
-                             1  0   1   0
-                             2  1   0   1
-        mutual_clusters (first iteration):
-            A0_B0 -> {}, A0_B1 -> {0, 1}, A1_B0 -> {2}, A1_B1 -> {}
-        mutual_clusters (final iteration):
-            A0_B0_C0 -> {}, A0_B0_C1 -> {}, A0_B1_C0 -> {0, 1}, A0_B1_C1 -> {},
-            A1_B0_C0 -> {}, A1_B0_C1 -> {2}, A1_B1_C0 -> {}, A1_B1_C1 -> {}
-    >>> df = pd.DataFrame({"A": [0, 0, 1], "B": [1, 1, 0], "C": [0, 0, 1]})
-    >>> compute_mutual_clusters_recursive(cluster_labels_df=df)
-    """
-    _num_of_layers = len(cluster_labels_df.columns)
-    # recursion base case: no layer left to be processed
-    if next_layer_idx == _num_of_layers:
-        return mutual_clusters
-    else:
-        _next_layer = cluster_labels_df.columns[next_layer_idx]
-        _updated_mutual_clusters = {}
-        if len(mutual_clusters) == 0:
-            # in this case, we need to create the keys for the dictionary from scratch
-            for _cluster_label in cluster_labels_df[_next_layer].unique():
-                _cluster_content = cluster_labels_df[
-                    cluster_labels_df[_next_layer] == _cluster_label
-                ][_next_layer].index
-                _key = str(_next_layer) + str(_cluster_label)
-                _updated_mutual_clusters[_key] = set(_cluster_content)
-            return compute_mutual_clusters_recursive(
-                cluster_labels_df, _updated_mutual_clusters, next_layer_idx + 1
-            )
-        else:
-            # in this case, we start from the current mutual clusters and compare with the current layer
-            _updated_mutual_clusters = {}
-            for mc_id, curr_mc in mutual_clusters.items():
-                for _cluster_label in cluster_labels_df[_next_layer].unique():
-                    _cluster_content = cluster_labels_df[
-                        cluster_labels_df[_next_layer] == _cluster_label
-                    ][_next_layer].index
-                    _new_key_suffix = str(_next_layer) + str(_cluster_label)
-                    _key = str(mc_id) + "_" + _new_key_suffix
-                    _new_set = curr_mc.intersection(set(_cluster_content))
-                    if len(_new_set) > 0:
-                        _updated_mutual_clusters[_key] = _new_set
-            return compute_mutual_clusters_recursive(
-                cluster_labels_df, _updated_mutual_clusters, next_layer_idx + 1
-            )
diff --git a/multilayer_alignment/null_models.py b/multilayer_alignment/null_models.py
index c2f627e..0046ed9 100644
--- a/multilayer_alignment/null_models.py
+++ b/multilayer_alignment/null_models.py
@@ -1,49 +1,51 @@
 import pandas as pd
 import numpy as np
 import os
-from scipy.stats import entropy
+from scipy.stats import entropy  # type: ignore
 from itertools import combinations
-from joblib import dump
+from joblib import dump  # type: ignore
 from functools import partial
-import typing
+from typing import Dict, List, Union
 
 import multiprocessing as mp
 from multiprocessing.pool import Pool
 from tqdm import tqdm
 
-from multilayer_alignment.alignment_score import compute_maximal_alignment_curve
+from multilayer_alignment.alignment_score import maximal_alignment_curve
 
 from multilayer_alignment.utils.logging import logger
 
 
-def get_null_model(cluster_labels_df: pd.DataFrame) -> pd.DataFrame:
+def get_null_model(opinions: Union[pd.DataFrame, pd.Series]) -> pd.DataFrame:
     """
-    :param cluster_labels_df: pd.DataFrame having one column per layer and one row per node,
+    :param opinions: pd.DataFrame having one column per layer and one row per node,
         where each element a_ij is an integer representing the cluster labels for node i at layer j
     :return: pd.DataFrame, having one column per layer and one row per node,
         where each element a_ij is an integer representing the cluster labels for node i at layer j
     """
     null = pd.DataFrame()
-    for layer_id in cluster_labels_df.columns:
-        _layer = cluster_labels_df[layer_id].fillna(9).values
-        null[layer_id] = np.random.permutation(_layer)
+    for layer_id in opinions.columns:
+        _layer = opinions[layer_id].fillna(9).values
+        null[layer_id] = np.random.permutation(_layer)  # type: ignore
 
     return null
 
 
 def _one_iter(
-    cluster_labels_df: pd.DataFrame, which_score: str = "ami", adjusted: bool = False
-) -> dict:
+    opinions: Union[pd.DataFrame, pd.Series],
+    which_score: str = "ami",
+    adjusted: bool = False,
+) -> Dict:
     """
-    :param cluster_labels_df: pd.DataFrame having one column per layer and one row per node,
+    :param opinions: pd.DataFrame having one column per layer and one row per node,
         where each element a_ij is an integer representing the cluster labels for node i at layer j
     :param which_score: str
     :param adjusted: bool
     :return: dict
     """
-    null = get_null_model(cluster_labels_df=cluster_labels_df)
+    null = get_null_model(opinions=opinions)
 
-    _full_res, _ = compute_maximal_alignment_curve(
+    _full_res, _ = maximal_alignment_curve(
         null, which_score=which_score, adjusted=adjusted
     )
     return _full_res
@@ -84,24 +86,24 @@ def random_full_alignment_curves(
             i += 1
 
 
-def expected_curve(cluster_labels_df: pd.DataFrame) -> typing.List:
+def expected_curve(opinions: Union[pd.DataFrame, pd.Series]) -> List[float]:
     """
-    :param cluster_labels_df: pd.DataFrame having one column per layer and one row per node,
+    :param opinions: pd.DataFrame having one column per layer and one row per node,
         where each element a_ij is an integer representing the cluster labels for node i at layer j
     :return: list of expected scores based on average NMI (normalized by arithmetic average)
     """
     _expected_best_scores = []
-    _layers = list(cluster_labels_df.columns)
+    _layers = list(opinions.columns)
 
-    _a = cluster_labels_df.copy()
+    _a = opinions.copy()
     _a["count"] = 1
 
     for length in range(2, len(_layers) + 1):
         logger.info(f"combinations of size {length}")
-        # Get all combinations of cluster_labels_df.columns of length "length"
+        # Get all combinations of opinions.columns of length "length"
         _columns_combinations = combinations(_layers, length)
 
-        best_score = 0
+        best_score = 0.0
 
         for l_comb in tqdm(_columns_combinations):
             l_mc = []
@@ -121,5 +123,5 @@ def expected_curve(cluster_labels_df: pd.DataFrame) -> typing.List:
     return _expected_best_scores
 
 
-def expected_curve_equal_sized_clusters(n_layers: int) -> typing.List:
+def expected_curve_equal_sized_clusters(n_layers: int) -> List[float]:
     return [2 / (1 + k) for k in range(2, n_layers + 1)]
diff --git a/multilayer_alignment/visualizations.py b/multilayer_alignment/visualizations.py
index c3fc29c..536d6e8 100644
--- a/multilayer_alignment/visualizations.py
+++ b/multilayer_alignment/visualizations.py
@@ -1,33 +1,33 @@
 import pandas as pd
 import numpy as np
-from joblib import load
-import matplotlib.pyplot as plt
-import matplotlib.markers as markers
-from mpl_toolkits.axes_grid1.inset_locator import inset_axes
+from joblib import load  # type: ignore
+import matplotlib.pyplot as plt  # type: ignore
+import matplotlib.markers as markers  # type: ignore
+from mpl_toolkits.axes_grid1.inset_locator import inset_axes  # type: ignore
 import random
-import seaborn as sns
+import seaborn as sns  # type: ignore
 import typing
 
-from multilayer_alignment.alignment_score import compute_maximal_alignment_curve
+from multilayer_alignment.alignment_score import maximal_alignment_curve
 from multilayer_alignment.null_models import expected_curve_equal_sized_clusters
 from multilayer_alignment.utils.logging import logger
 
 
 def plot_maximal_alignment_curve(
-    cluster_labels_df: pd.DataFrame,
+    opinions: pd.DataFrame,
     which_score: str = "nmi",
     adjusted: bool = False,
 ) -> plt.Figure:
     """
-    :param cluster_labels_df: pd.DataFrame having one column per layer and one row per node,
+    :param opinions: pd.DataFrame having one column per layer and one row per node,
         where each element a_ij is an integer representing the cluster labels for node i at layer j
         and column names are layers names
     :param which_score: str, one of "nmi" or "ami"
     :param adjusted: bool, default: False
     :return: plt.Figure with 2 subplots (1 row x 2 columns)
     """
-    _, res = compute_maximal_alignment_curve(
-        cluster_labels_df=cluster_labels_df, which_score=which_score, adjusted=adjusted
+    _, res = maximal_alignment_curve(
+        opinions=opinions, which_score=which_score, adjusted=adjusted
     )
     combination_sizes = []
     anmi_scores = []
@@ -60,13 +60,13 @@ def plot_maximal_alignment_curve(
 
 
 def plot_full_alignment_curve(
-    cluster_labels_df: typing.Optional[pd.DataFrame],
+    opinions: typing.Optional[pd.DataFrame],
     which_score: typing.Optional[str],
     adjusted: typing.Optional[bool],
     full_dump_path: typing.Optional[str],
 ) -> plt.Figure:
     """
-    :param cluster_labels_df: pd.DataFrame having one column per layer and one row per node,
+    :param opinions: pd.DataFrame having one column per layer and one row per node,
         where each element a_ij is an integer representing the cluster labels for node i at layer j
         and column names are layers names
     :param which_score: str, one of "nmi" or "ami" or None
@@ -78,19 +78,19 @@ def plot_full_alignment_curve(
         all_results = load(full_dump_path)
     else:
         assert (
-            cluster_labels_df is not None
-            and which_score is not None
-            and adjusted is not None
+            opinions is not None and which_score is not None and adjusted is not None
         ), "not enough inputs"
-        all_results, _ = compute_maximal_alignment_curve(
-            cluster_labels_df=cluster_labels_df,
+        all_results, _ = maximal_alignment_curve(
+            opinions=opinions,
             which_score=which_score,
             adjusted=adjusted,
         )
 
-    points = [(k.split("+")[0], v[0], k.split("+")[1:]) for k, v in all_results.items()]
-    points = pd.DataFrame(points)
-    top = points.sort_values(by=[1, 0], ascending=False).groupby(0).head(1)
+    _points = [
+        (k.split("+")[0], v[0], k.split("+")[1:]) for k, v in all_results.items()
+    ]
+    points = pd.DataFrame(_points)
+    top = points.sort_values(by=[1, 0], ascending=False).groupby(0).head(1)  # type: ignore
 
     logger.info(f"Area under the curve: {np.trapz(top[1], dx=1 / (len(top) - 1))}")
 
@@ -113,11 +113,11 @@ def plot_full_alignment_curve(
     # Annotate each point with its label
     for i, label in top[[2]].iterrows():
         label = label[2]
-        text = [lab.replace("_", " ") for lab in label]
-        text = "\n".join(text)
+        _text = [lab.replace("_", " ") for lab in label]
+        text = "\n".join(_text)
         plt.annotate(
             text,
-            (x_top[i], y_top[i]),
+            (x_top[i], y_top[i]),  # type: ignore
             textcoords="offset points",
             xytext=(0, 10),
             ha="left",
@@ -147,23 +147,23 @@ def plot_full_alignment_with_null_models(
     full_result: str, full_null_path: str
 ) -> plt.Figure:
     r = load(full_result)
-    points = [(k.split("+")[0], v[0], k.split("+")[1:]) for k, v in r.items()]
-    points = pd.DataFrame(points)
+    _points = [(k.split("+")[0], v[0], k.split("+")[1:]) for k, v in r.items()]
+    points = pd.DataFrame(_points)
 
-    points_df = []
+    _dfs = []
     for i in range(10):
         r = load(f"{full_null_path}/null_{i}")
         _points = [(k.split("+")[0], v[0], k.split("+")[1:]) for k, v in r.items()]
-        points_df.append(pd.DataFrame(_points))
+        _dfs.append(pd.DataFrame(_points))
 
-    points_df = pd.concat(points_df, ignore_index=True)
+    points_df = pd.concat(_dfs, ignore_index=True)
 
-    top = points.sort_values(by=[1, 0], ascending=False).groupby(0).head(1)
+    top = points.sort_values(by=[1, 0], ascending=False).groupby(0).head(1)  # type: ignore
     x_top = top[0]
-    x_top = [int(v) for v in x_top]
+    x_top = [int(v) for v in x_top]  # type: ignore
     y_top = top[1]
 
-    _strip = points[~points.index.isin(top.index)]
+    _strip = points[~points.index.isin(top.index)]  # type: ignore
 
     null_avg = points_df.groupby(0)[1].mean().to_dict()
     y_top_ = y_top.values - np.array([null_avg[k] for k in null_avg.keys()])
@@ -172,12 +172,12 @@ def plot_full_alignment_with_null_models(
     null_lower_q = points_df.groupby(0)[1].quantile(q=0.025).to_dict()
     sig = [
         (_i, v - null_avg[_i])
-        for _i, v in _strip[[0, 1]].values
+        for _i, v in _strip[[0, 1]].values  # type: ignore
         if v > null_upper_q[_i] or v < null_lower_q[_i]
     ]
     not_sig = [
         (_i, v - null_avg[_i])
-        for _i, v in _strip[[0, 1]].values
+        for _i, v in _strip[[0, 1]].values  # type: ignore
         if null_lower_q[_i] <= v <= null_upper_q[_i]
     ]
 
@@ -191,10 +191,10 @@ def plot_full_alignment_with_null_models(
         alpha=1.0,
     )
     # Annotate each point with its label
-    for j, (i, label) in enumerate(top[[2]].iterrows()):
+    for j, (i, label) in enumerate(top[[2]].iterrows()):  # type: ignore
         label = label[2]
-        text = [lab.replace("_", " ") for lab in label]
-        text = "\n".join(text)
+        _text = [lab.replace("_", " ") for lab in label]
+        text = "\n".join(_text)
         ax.annotate(
             text,
             (x_top[j], y_top_[j]),
diff --git a/ruff.toml b/ruff.toml
new file mode 100644
index 0000000..d28e492
--- /dev/null
+++ b/ruff.toml
@@ -0,0 +1,77 @@
+# Exclude a variety of commonly ignored directories.
+exclude = [
+    ".bzr",
+    ".direnv",
+    ".eggs",
+    ".git",
+    ".git-rewrite",
+    ".hg",
+    ".ipynb_checkpoints",
+    ".mypy_cache",
+    ".nox",
+    ".pants.d",
+    ".pyenv",
+    ".pytest_cache",
+    ".pytype",
+    ".ruff_cache",
+    ".svn",
+    ".tox",
+    ".venv",
+    ".vscode",
+    "__pypackages__",
+    "_build",
+    "buck-out",
+    "build",
+    "dist",
+    "node_modules",
+    "site-packages",
+    "venv",
+]
+
+# Same as Black.
+line-length = 88
+indent-width = 4
+
+# Assume Python 3.8
+target-version = "py38"
+
+[lint]
+# Enable Pyflakes (`F`) and a subset of the pycodestyle (`E`)  codes by default.
+# Unlike Flake8, Ruff doesn't enable pycodestyle warnings (`W`) or
+# McCabe complexity (`C901`) by default.
+select = ["E4", "E7", "E9", "F"]
+ignore = []
+
+# Allow fix for all enabled rules (when `--fix`) is provided.
+fixable = ["ALL"]
+unfixable = []
+
+# Allow unused variables when underscore-prefixed.
+dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
+
+[format]
+# Like Black, use double quotes for strings.
+quote-style = "double"
+
+# Like Black, indent with spaces, rather than tabs.
+indent-style = "space"
+
+# Like Black, respect magic trailing commas.
+skip-magic-trailing-comma = false
+
+# Like Black, automatically detect the appropriate line ending.
+line-ending = "auto"
+
+# Enable auto-formatting of code examples in docstrings. Markdown,
+# reStructuredText code/literal blocks and doctests are all supported.
+#
+# This is currently disabled by default, but it is planned for this
+# to be opt-out in the future.
+docstring-code-format = false
+
+# Set the line length limit used when formatting code snippets in
+# docstrings.
+#
+# This only has an effect when the `docstring-code-format` setting is
+# enabled.
+docstring-code-line-length = "dynamic"
diff --git a/setup.py b/setup.py
index 0296d75..6198d29 100644
--- a/setup.py
+++ b/setup.py
@@ -1,4 +1,4 @@
-from setuptools import setup, find_packages
+from setuptools import setup, find_packages  # type: ignore
 
 setup(
     name="multilayer_alignment",
diff --git a/tests/test_compute_maximal_alignment_curve.py b/tests/test_compute_maximal_alignment_curve.py
index 20e5176..5336b99 100644
--- a/tests/test_compute_maximal_alignment_curve.py
+++ b/tests/test_compute_maximal_alignment_curve.py
@@ -2,7 +2,7 @@
 
 import pandas as pd
 
-from multilayer_alignment.alignment_score import compute_maximal_alignment_curve
+from multilayer_alignment.alignment_score import maximal_alignment_curve
 
 
 class TestComputeMaximalAlignmentCurve(unittest.TestCase):
@@ -19,7 +19,7 @@ def test_on_empty(self):
         compute_maximal_alignment_curve returns a tuple with two dictionaries
         """
         _a = pd.DataFrame()
-        _resall, _res0 = compute_maximal_alignment_curve(_a)
+        _resall, _res0 = maximal_alignment_curve(_a)
         self.assertIsInstance(
             _resall,
             dict,
@@ -48,7 +48,7 @@ def test_on_one_layer(self):
         compute_maximal_alignment_curve returns a tuple with two dictionaries
         """
         _a = pd.DataFrame({"A": [0, 1, 2]})
-        _resall, _res0 = compute_maximal_alignment_curve(_a)
+        _resall, _res0 = maximal_alignment_curve(_a)
         self.assertIsInstance(
             _resall,
             dict,
diff --git a/tests/test_compute_multilayer_alignment_score.py b/tests/test_compute_multilayer_alignment_score.py
index b087030..00fc80c 100644
--- a/tests/test_compute_multilayer_alignment_score.py
+++ b/tests/test_compute_multilayer_alignment_score.py
@@ -2,7 +2,7 @@
 
 import pandas as pd
 
-from multilayer_alignment.alignment_score import compute_multilayer_alignment_score
+from multilayer_alignment.alignment_score import multilayer_alignment_score
 
 
 class TestComputeMultilayerAlignmentScore(unittest.TestCase):
@@ -20,7 +20,7 @@ def test_on_empty(self):
         """
         _a = pd.DataFrame({"A": [0, 1, 2]})
         _labels = ["a", "b", "c"]
-        _res0 = compute_multilayer_alignment_score(_a, _labels)
+        _res0 = multilayer_alignment_score(_a, _labels)
         self.assertIsInstance(
             _res0,
             float,
diff --git a/tests/test_get_mutual_clusters_labels.py b/tests/test_get_consensus_labels.py
similarity index 51%
rename from tests/test_get_mutual_clusters_labels.py
rename to tests/test_get_consensus_labels.py
index 1ca777e..14814ca 100644
--- a/tests/test_get_mutual_clusters_labels.py
+++ b/tests/test_get_consensus_labels.py
@@ -2,51 +2,51 @@
 
 import pandas as pd
 
-from multilayer_alignment.mutual_clusters import get_mutual_clusters_labels
+from multilayer_alignment.consensus import get_consensus_labels
 
 
-class TestGetMutualClustersLabels(unittest.TestCase):
+class TestGetConsensusLabels(unittest.TestCase):
     """
-    Test functionality of mutual_clusters.get_mutual_clusters_labels()
+    Test functionality of mutual_clusters.get_consensus_labels()
     ------------
     Example
     ------------
-    >>> python3 -m unittest -v tests.test_get_mutual_clusters_labels
+    >>> python3 -m unittest -v tests.test_get_consensus_labels
     """
 
     def test_on_empty(self):
         """
-        get_mutual_clusters_labels returns a pd.DataFrame
+        get_consensus_labels returns a pd.DataFrame
         """
         _a = pd.DataFrame()
-        _res0 = get_mutual_clusters_labels(_a)
+        _res0 = get_consensus_labels(_a)
         self.assertIsInstance(
             _res0,
             pd.DataFrame,
-            f"""get_mutual_clusters_labels should return a pd.DataFrame,
+            f"""get_consensus_labels should return a pd.DataFrame,
             but returned {type(_res0)}""",
         )
         self.assertTrue(
             _res0.empty,
-            f"""get_mutual_clusters_labels called on empty dictionary should return
+            f"""get_consensus_labels called on empty dictionary should return
             an empty pd.DataFrame, but returned {_res0}""",
         )
 
     def test_on_simple_sets(self):
         """
-        get_mutual_clusters_labels returns a pd.DataFrame
+        get_consensus_labels returns a pd.DataFrame
         """
         _a = {"A0_B1_C0": {0, 1}, "A1_B0_C1": {2}, "A1_B1_C0": {3}}
-        _res0 = get_mutual_clusters_labels(_a)
+        _res0 = get_consensus_labels(_a)
         self.assertIsInstance(
             _res0,
             pd.DataFrame,
-            f"""get_mutual_clusters_labels should return a pd.DataFrame,
+            f"""get_consensus_labels should return a pd.DataFrame,
             but returned {type(_res0)}""",
         )
         self.assertFalse(
             _res0.empty,
-            f"""get_mutual_clusters_labels called on non-empty dictionary should return
+            f"""get_consensus_labels called on non-empty dictionary should return
             a non-empty pd.DataFrame, but returned {_res0}""",
         )
 
diff --git a/tests/test_compute_mutual_clusters.py b/tests/test_get_consensus_partition.py
similarity index 68%
rename from tests/test_compute_mutual_clusters.py
rename to tests/test_get_consensus_partition.py
index b8d4417..e0b168b 100644
--- a/tests/test_compute_mutual_clusters.py
+++ b/tests/test_get_consensus_partition.py
@@ -2,223 +2,223 @@
 
 import pandas as pd
 
-from multilayer_alignment.mutual_clusters import compute_mutual_clusters
+from multilayer_alignment.consensus import get_consensus_partition
 
 
-class TestComputeMutualClusters(unittest.TestCase):
+class TestGetConsensusPartition(unittest.TestCase):
     """
-    Test functionality of mutual_clusters.compute_mutual_clusters()
+    Test functionality of mutual_clusters.get_consensus_partition()
     ------------
     Example
     ------------
-    >>> python3 -m unittest -v tests.test_compute_mutual_clusters
+    >>> python3 -m unittest -v tests.test_get_consensus_partition
     """
 
     def test_on_empty(self):
         """
-        compute_mutual_clusters returns a dictionary
+        get_consensus_partition returns a dictionary
         """
         _a = pd.DataFrame()
-        _res0 = compute_mutual_clusters(cluster_labels_df=_a)
+        _res0 = get_consensus_partition(opinions=_a)
         self.assertIsInstance(
             _res0,
             dict,
-            f"""compute_mutual_clusters should return a dictionary, but returned {type(_res0)}""",
+            f"""get_consensus_partition should return a dictionary, but returned {type(_res0)}""",
         )
         self.assertEqual(
             _res0,
             dict(),
-            f"""compute_mutual_clusters called on empty dataframe should return an empty dictionary,
+            f"""get_consensus_partition called on empty dataframe should return an empty dictionary,
             but returned {_res0}""",
         )
 
     def test_on_one_layer(self):
         """
-        compute_mutual_clusters returns a dictionary, and returns all the nodes
+        get_consensus_partition returns a dictionary, and returns all the nodes
         """
         _a = pd.DataFrame({"A": [0, 1, 2]})
         _all_nodes = list(_a.index)
-        _res0 = compute_mutual_clusters(cluster_labels_df=_a)
+        _res0 = get_consensus_partition(opinions=_a)
         _returned_nodes = [n for mc in _res0.values() for n in mc]
         _expected0 = {"A0": {0}, "A1": {1}, "A2": {2}}
 
         self.assertIsInstance(
             _res0,
             dict,
-            f"""compute_mutual_clusters should return a dictionary, but returned {type(_res0)}""",
+            f"""get_consensus_partition should return a dictionary, but returned {type(_res0)}""",
         )
 
         self.assertNotEqual(
             _res0,
             dict(),
-            f"""compute_mutual_clusters called on non-empty dataframe should return a non-empty dictionary,
+            f"""get_consensus_partition called on non-empty dataframe should return a non-empty dictionary,
             but returned {_res0}""",
         )
 
         self.assertDictEqual(
             _res0,
             _expected0,
-            f"""compute_mutual_clusters called on non-empty dataframe should return
+            f"""get_consensus_partition called on non-empty dataframe should return
             the expected non-empty dictionary, but returned {_res0}""",
         )
 
         self.assertSetEqual(
             set(_all_nodes),
             set(_returned_nodes),
-            """compute_mutual_clusters called on non-empty dataframe should return all nodes""",
+            """get_consensus_partition called on non-empty dataframe should return all nodes""",
         )
 
     def test_on_two_layers_0(self):
         """
-        compute_mutual_clusters returns a dictionary, and returns all the nodes
+        get_consensus_partition returns a dictionary, and returns all the nodes
         """
         _a = pd.DataFrame({"A": [0, 1, 2], "B": [3, 4, 5]})
         _all_nodes = list(_a.index)
-        _res0 = compute_mutual_clusters(cluster_labels_df=_a)
+        _res0 = get_consensus_partition(opinions=_a)
         _returned_nodes = [n for mc in _res0.values() for n in mc]
         _expected0 = {"A0_B3": {0}, "A1_B4": {1}, "A2_B5": {2}}
 
         self.assertIsInstance(
             _res0,
             dict,
-            f"""compute_mutual_clusters should return a dictionary, but returned {type(_res0)}""",
+            f"""get_consensus_partition should return a dictionary, but returned {type(_res0)}""",
         )
 
         self.assertNotEqual(
             _res0,
             dict(),
-            f"""compute_mutual_clusters called on non-empty dataframe should return a
+            f"""get_consensus_partition called on non-empty dataframe should return a
             non-empty dictionary, but returned {_res0}""",
         )
 
         self.assertDictEqual(
             _res0,
             _expected0,
-            f"""compute_mutual_clusters called on non-empty dataframe should return the expected non-empty
+            f"""get_consensus_partition called on non-empty dataframe should return the expected non-empty
             dictionary, but returned {_res0}""",
         )
 
         self.assertSetEqual(
             set(_all_nodes),
             set(_returned_nodes),
-            """compute_mutual_clusters called on non-empty dataframe should return all nodes""",
+            """get_consensus_partition called on non-empty dataframe should return all nodes""",
         )
 
     def test_on_two_layers_1(self):
         """
-        compute_mutual_clusters returns a dictionary, and returns all the nodes
+        get_consensus_partition returns a dictionary, and returns all the nodes
         """
         _a = pd.DataFrame({"A": [0, 0, 2, 2], "B": [0, 0, 2, 2]})
         _all_nodes = list(_a.index)
-        _res0 = compute_mutual_clusters(cluster_labels_df=_a)
+        _res0 = get_consensus_partition(opinions=_a)
         _returned_nodes = [n for mc in _res0.values() for n in mc]
         _expected0 = {"A0_B0": {0, 1}, "A2_B2": {2, 3}}
 
         self.assertIsInstance(
             _res0,
             dict,
-            f"""compute_mutual_clusters should return a dictionary, but returned {type(_res0)}""",
+            f"""get_consensus_partition should return a dictionary, but returned {type(_res0)}""",
         )
 
         self.assertNotEqual(
             _res0,
             dict(),
-            f"""compute_mutual_clusters called on non-empty dataframe should return a
+            f"""get_consensus_partition called on non-empty dataframe should return a
             non-empty dictionary, but returned {_res0}""",
         )
 
         self.assertDictEqual(
             _res0,
             _expected0,
-            f"""compute_mutual_clusters called on non-empty dataframe should return
+            f"""get_consensus_partition called on non-empty dataframe should return
             the expected non-empty dictionary, but returned {_res0}""",
         )
 
         self.assertSetEqual(
             set(_all_nodes),
             set(_returned_nodes),
-            """compute_mutual_clusters called on non-empty dataframe should return all nodes""",
+            """get_consensus_partition called on non-empty dataframe should return all nodes""",
         )
 
     def test_on_two_layers_2(self):
         """
-        compute_mutual_clusters returns a dictionary, and returns all the nodes
+        get_consensus_partition returns a dictionary, and returns all the nodes
         """
         _a = pd.DataFrame({"A": [3, 3, 3, 3], "B": [0, 0, 2, 2]})
         _all_nodes = list(_a.index)
-        _res0 = compute_mutual_clusters(cluster_labels_df=_a)
+        _res0 = get_consensus_partition(opinions=_a)
         _returned_nodes = [n for mc in _res0.values() for n in mc]
         _expected0 = {"A3_B0": {0, 1}, "A3_B2": {2, 3}}
 
         self.assertIsInstance(
             _res0,
             dict,
-            f"""compute_mutual_clusters should return a dictionary, but returned {type(_res0)}""",
+            f"""get_consensus_partition should return a dictionary, but returned {type(_res0)}""",
         )
 
         self.assertNotEqual(
             _res0,
             dict(),
-            f"""compute_mutual_clusters called on non-empty dataframe should return a
+            f"""get_consensus_partition called on non-empty dataframe should return a
             non-empty dictionary, but returned {_res0}""",
         )
 
         self.assertDictEqual(
             _res0,
             _expected0,
-            f"""compute_mutual_clusters called on non-empty dataframe should return
+            f"""get_consensus_partition called on non-empty dataframe should return
             the expected non-empty dictionary, but returned {_res0}""",
         )
 
         self.assertSetEqual(
             set(_all_nodes),
             set(_returned_nodes),
-            """compute_mutual_clusters called on non-empty dataframe should return all nodes""",
+            """get_consensus_partition called on non-empty dataframe should return all nodes""",
         )
 
     def test_on_three_layers_0(self):
         """
-        compute_mutual_clusters returns a dictionary, and returns all the nodes
+        get_consensus_partition returns a dictionary, and returns all the nodes
         """
         _a = pd.DataFrame({"A": [0, 0, 2, 2], "B": [0, 0, 2, 2], "C": [0, 0, 2, 2]})
         _all_nodes = list(_a.index)
-        _res0 = compute_mutual_clusters(cluster_labels_df=_a)
+        _res0 = get_consensus_partition(opinions=_a)
         _returned_nodes = [n for mc in _res0.values() for n in mc]
         _expected0 = {"A0_B0_C0": {0, 1}, "A2_B2_C2": {2, 3}}
 
         self.assertIsInstance(
             _res0,
             dict,
-            f"""compute_mutual_clusters should return a dictionary, but returned {type(_res0)}""",
+            f"""get_consensus_partition should return a dictionary, but returned {type(_res0)}""",
         )
 
         self.assertNotEqual(
             _res0,
             dict(),
-            f"""compute_mutual_clusters called on non-empty dataframe should return a
+            f"""get_consensus_partition called on non-empty dataframe should return a
             non-empty dictionary, but returned {_res0}""",
         )
 
         self.assertDictEqual(
             _res0,
             _expected0,
-            f"""compute_mutual_clusters called on non-empty dataframe should return
+            f"""get_consensus_partition called on non-empty dataframe should return
             the expected non-empty dictionary, but returned {_res0}""",
         )
 
         self.assertSetEqual(
             set(_all_nodes),
             set(_returned_nodes),
-            """compute_mutual_clusters called on non-empty dataframe should return all nodes""",
+            """get_consensus_partition called on non-empty dataframe should return all nodes""",
         )
 
     def test_on_three_layers_1(self):
         """
-        compute_mutual_clusters returns a dictionary, and returns all the nodes
+        get_consensus_partition returns a dictionary, and returns all the nodes
         """
         _a = pd.DataFrame({"A": [0, 0, 1, 1], "B": [0, 1, 0, 1], "C": [1, 0, 1, 0]})
         _all_nodes = list(_a.index)
-        _res0 = compute_mutual_clusters(cluster_labels_df=_a)
+        _res0 = get_consensus_partition(opinions=_a)
         _returned_nodes = [n for mc in _res0.values() for n in mc]
         _expected0 = {
             "A0_B0_C1": {0},
@@ -230,63 +230,63 @@ def test_on_three_layers_1(self):
         self.assertIsInstance(
             _res0,
             dict,
-            f"""compute_mutual_clusters should return a dictionary, but returned {type(_res0)}""",
+            f"""get_consensus_partition should return a dictionary, but returned {type(_res0)}""",
         )
 
         self.assertNotEqual(
             _res0,
             dict(),
-            f"""compute_mutual_clusters called on non-empty dataframe should return a non-empty dictionary,
+            f"""get_consensus_partition called on non-empty dataframe should return a non-empty dictionary,
             but returned {_res0}""",
         )
 
         self.assertDictEqual(
             _res0,
             _expected0,
-            f"""compute_mutual_clusters called on non-empty dataframe should return
+            f"""get_consensus_partition called on non-empty dataframe should return
             the expected non-empty dictionary, but returned {_res0}""",
         )
 
         self.assertSetEqual(
             set(_all_nodes),
             set(_returned_nodes),
-            """compute_mutual_clusters called on non-empty dataframe should return all nodes""",
+            """get_consensus_partition called on non-empty dataframe should return all nodes""",
         )
 
     def test_on_three_layers_2(self):
         """
-        compute_mutual_clusters returns a dictionary, and returns all the nodes
+        get_consensus_partition returns a dictionary, and returns all the nodes
         """
         _a = pd.DataFrame({"A": [0, 0, 1, 1], "B": [1, 1, 0, 1], "C": [0, 0, 1, 0]})
         _all_nodes = list(_a.index)
-        _res0 = compute_mutual_clusters(cluster_labels_df=_a)
+        _res0 = get_consensus_partition(opinions=_a)
         _returned_nodes = [n for mc in _res0.values() for n in mc]
         _expected0 = {"A0_B1_C0": {0, 1}, "A1_B0_C1": {2}, "A1_B1_C0": {3}}
 
         self.assertIsInstance(
             _res0,
             dict,
-            f"""compute_mutual_clusters should return a dictionary, but returned {type(_res0)}""",
+            f"""get_consensus_partition should return a dictionary, but returned {type(_res0)}""",
         )
 
         self.assertNotEqual(
             _res0,
             dict(),
-            f"""compute_mutual_clusters called on non-empty dataframe should return a non-empty dictionary,
+            f"""get_consensus_partition called on non-empty dataframe should return a non-empty dictionary,
             but returned {_res0}""",
         )
 
         self.assertDictEqual(
             _res0,
             _expected0,
-            f"""compute_mutual_clusters called on non-empty dataframe should return the expected non-empty
+            f"""get_consensus_partition called on non-empty dataframe should return the expected non-empty
                 dictionary, but returned {_res0}""",
         )
 
         self.assertSetEqual(
             set(_all_nodes),
             set(_returned_nodes),
-            """compute_mutual_clusters called on non-empty dataframe should return all nodes""",
+            """get_consensus_partition called on non-empty dataframe should return all nodes""",
         )
 
 
diff --git a/tests/test_compute_mutual_clusters_recursive.py b/tests/test_get_consensus_partition_recursive.py
similarity index 68%
rename from tests/test_compute_mutual_clusters_recursive.py
rename to tests/test_get_consensus_partition_recursive.py
index f265373..93c1880 100644
--- a/tests/test_compute_mutual_clusters_recursive.py
+++ b/tests/test_get_consensus_partition_recursive.py
@@ -2,223 +2,223 @@
 
 import pandas as pd
 
-from multilayer_alignment.mutual_clusters import compute_mutual_clusters_recursive
+from multilayer_alignment.consensus import get_consensus_partition_recursive
 
 
-class TestComputeMutualClustersRecursive(unittest.TestCase):
+class TestGetConsensusPartitionRecursive(unittest.TestCase):
     """
-    Test functionality of mutual_clusters.compute_mutual_clusters_recursive()
+    Test functionality of consensus.get_consensus_partition_recursive()
     ------------
     Example
     ------------
-    >>> python3 -m unittest -v tests.test_compute_mutual_clusters_recursive
+    >>> python3 -m unittest -v tests.test_get_consensus_partition_recursive
     """
 
     def test_on_empty(self):
         """
-        compute_mutual_clusters_recursive returns a dictionary
+        get_consensus_partition_recursive returns a dictionary
         """
         _a = pd.DataFrame()
-        _res0 = compute_mutual_clusters_recursive(cluster_labels_df=_a)
+        _res0 = get_consensus_partition_recursive(opinions=_a)
         self.assertIsInstance(
             _res0,
             dict,
-            f"""compute_mutual_clusters_recursive should return a dictionary, but returned {type(_res0)}""",
+            f"""get_consensus_partition_recursive should return a dictionary, but returned {type(_res0)}""",
         )
         self.assertEqual(
             _res0,
             dict(),
-            f"""compute_mutual_clusters_recursive called on empty dataframe should return an empty dictionary,
+            f"""get_consensus_partition_recursive called on empty dataframe should return an empty dictionary,
             but returned {_res0}""",
         )
 
     def test_on_one_layer(self):
         """
-        compute_mutual_clusters_recursive returns a dictionary, and returns all the nodes
+        get_consensus_partition_recursive returns a dictionary, and returns all the nodes
         """
         _a = pd.DataFrame({"A": [0, 1, 2]})
         _all_nodes = list(_a.index)
-        _res0 = compute_mutual_clusters_recursive(cluster_labels_df=_a)
+        _res0 = get_consensus_partition_recursive(opinions=_a)
         _returned_nodes = [n for mc in _res0.values() for n in mc]
         _expected0 = {"A0": {0}, "A1": {1}, "A2": {2}}
 
         self.assertIsInstance(
             _res0,
             dict,
-            f"""compute_mutual_clusters_recursive should return a dictionary, but returned {type(_res0)}""",
+            f"""get_consensus_partition_recursive should return a dictionary, but returned {type(_res0)}""",
         )
 
         self.assertNotEqual(
             _res0,
             dict(),
-            f"""compute_mutual_clusters_recursive called on non-empty dataframe should return a non-empty dictionary,
+            f"""get_consensus_partition_recursive called on non-empty dataframe should return a non-empty dictionary,
             but returned {_res0}""",
         )
 
         self.assertDictEqual(
             _res0,
             _expected0,
-            f"""compute_mutual_clusters_recursive called on non-empty dataframe should return
+            f"""get_consensus_partition_recursive called on non-empty dataframe should return
             the expected non-empty dictionary, but returned {_res0}""",
         )
 
         self.assertSetEqual(
             set(_all_nodes),
             set(_returned_nodes),
-            """compute_mutual_clusters_recursive called on non-empty dataframe should return all nodes""",
+            """get_consensus_partition_recursive called on non-empty dataframe should return all nodes""",
         )
 
     def test_on_two_layers_0(self):
         """
-        compute_mutual_clusters_recursive returns a dictionary, and returns all the nodes
+        get_consensus_partition_recursive returns a dictionary, and returns all the nodes
         """
         _a = pd.DataFrame({"A": [0, 1, 2], "B": [3, 4, 5]})
         _all_nodes = list(_a.index)
-        _res0 = compute_mutual_clusters_recursive(cluster_labels_df=_a)
+        _res0 = get_consensus_partition_recursive(opinions=_a)
         _returned_nodes = [n for mc in _res0.values() for n in mc]
         _expected0 = {"A0_B3": {0}, "A1_B4": {1}, "A2_B5": {2}}
 
         self.assertIsInstance(
             _res0,
             dict,
-            f"""compute_mutual_clusters_recursive should return a dictionary, but returned {type(_res0)}""",
+            f"""get_consensus_partition_recursive should return a dictionary, but returned {type(_res0)}""",
         )
 
         self.assertNotEqual(
             _res0,
             dict(),
-            f"""compute_mutual_clusters_recursive called on non-empty dataframe should return a
+            f"""get_consensus_partition_recursive called on non-empty dataframe should return a
             non-empty dictionary, but returned {_res0}""",
         )
 
         self.assertDictEqual(
             _res0,
             _expected0,
-            f"""compute_mutual_clusters_recursive called on non-empty dataframe should return the expected non-empty
+            f"""get_consensus_partition_recursive called on non-empty dataframe should return the expected non-empty
             dictionary, but returned {_res0}""",
         )
 
         self.assertSetEqual(
             set(_all_nodes),
             set(_returned_nodes),
-            """compute_mutual_clusters_recursive called on non-empty dataframe should return all nodes""",
+            """get_consensus_partition_recursive called on non-empty dataframe should return all nodes""",
         )
 
     def test_on_two_layers_1(self):
         """
-        compute_mutual_clusters_recursive returns a dictionary, and returns all the nodes
+        get_consensus_partition_recursive returns a dictionary, and returns all the nodes
         """
         _a = pd.DataFrame({"A": [0, 0, 2, 2], "B": [0, 0, 2, 2]})
         _all_nodes = list(_a.index)
-        _res0 = compute_mutual_clusters_recursive(cluster_labels_df=_a)
+        _res0 = get_consensus_partition_recursive(opinions=_a)
         _returned_nodes = [n for mc in _res0.values() for n in mc]
         _expected0 = {"A0_B0": {0, 1}, "A2_B2": {2, 3}}
 
         self.assertIsInstance(
             _res0,
             dict,
-            f"""compute_mutual_clusters_recursive should return a dictionary, but returned {type(_res0)}""",
+            f"""get_consensus_partition_recursive should return a dictionary, but returned {type(_res0)}""",
         )
 
         self.assertNotEqual(
             _res0,
             dict(),
-            f"""compute_mutual_clusters_recursive called on non-empty dataframe should return a
+            f"""get_consensus_partition_recursive called on non-empty dataframe should return a
             non-empty dictionary, but returned {_res0}""",
         )
 
         self.assertDictEqual(
             _res0,
             _expected0,
-            f"""compute_mutual_clusters_recursive called on non-empty dataframe should return
+            f"""get_consensus_partition_recursive called on non-empty dataframe should return
             the expected non-empty dictionary, but returned {_res0}""",
         )
 
         self.assertSetEqual(
             set(_all_nodes),
             set(_returned_nodes),
-            """compute_mutual_clusters_recursive called on non-empty dataframe should return all nodes""",
+            """get_consensus_partition_recursive called on non-empty dataframe should return all nodes""",
         )
 
     def test_on_two_layers_2(self):
         """
-        compute_mutual_clusters_recursive returns a dictionary, and returns all the nodes
+        get_consensus_partition_recursive returns a dictionary, and returns all the nodes
         """
         _a = pd.DataFrame({"A": [3, 3, 3, 3], "B": [0, 0, 2, 2]})
         _all_nodes = list(_a.index)
-        _res0 = compute_mutual_clusters_recursive(cluster_labels_df=_a)
+        _res0 = get_consensus_partition_recursive(opinions=_a)
         _returned_nodes = [n for mc in _res0.values() for n in mc]
         _expected0 = {"A3_B0": {0, 1}, "A3_B2": {2, 3}}
 
         self.assertIsInstance(
             _res0,
             dict,
-            f"""compute_mutual_clusters_recursive should return a dictionary, but returned {type(_res0)}""",
+            f"""get_consensus_partition_recursive should return a dictionary, but returned {type(_res0)}""",
         )
 
         self.assertNotEqual(
             _res0,
             dict(),
-            f"""compute_mutual_clusters_recursive called on non-empty dataframe should return a
+            f"""get_consensus_partition_recursive called on non-empty dataframe should return a
             non-empty dictionary, but returned {_res0}""",
         )
 
         self.assertDictEqual(
             _res0,
             _expected0,
-            f"""compute_mutual_clusters_recursive called on non-empty dataframe should return
+            f"""get_consensus_partition_recursive called on non-empty dataframe should return
             the expected non-empty dictionary, but returned {_res0}""",
         )
 
         self.assertSetEqual(
             set(_all_nodes),
             set(_returned_nodes),
-            """compute_mutual_clusters_recursive called on non-empty dataframe should return all nodes""",
+            """get_consensus_partition_recursive called on non-empty dataframe should return all nodes""",
         )
 
     def test_on_three_layers_0(self):
         """
-        compute_mutual_clusters_recursive returns a dictionary, and returns all the nodes
+        get_consensus_partition_recursive returns a dictionary, and returns all the nodes
         """
         _a = pd.DataFrame({"A": [0, 0, 2, 2], "B": [0, 0, 2, 2], "C": [0, 0, 2, 2]})
         _all_nodes = list(_a.index)
-        _res0 = compute_mutual_clusters_recursive(cluster_labels_df=_a)
+        _res0 = get_consensus_partition_recursive(opinions=_a)
         _returned_nodes = [n for mc in _res0.values() for n in mc]
         _expected0 = {"A0_B0_C0": {0, 1}, "A2_B2_C2": {2, 3}}
 
         self.assertIsInstance(
             _res0,
             dict,
-            f"""compute_mutual_clusters_recursive should return a dictionary, but returned {type(_res0)}""",
+            f"""get_consensus_partition_recursive should return a dictionary, but returned {type(_res0)}""",
         )
 
         self.assertNotEqual(
             _res0,
             dict(),
-            f"""compute_mutual_clusters_recursive called on non-empty dataframe should return a
+            f"""get_consensus_partition_recursive called on non-empty dataframe should return a
             non-empty dictionary, but returned {_res0}""",
         )
 
         self.assertDictEqual(
             _res0,
             _expected0,
-            f"""compute_mutual_clusters_recursive called on non-empty dataframe should return
+            f"""get_consensus_partition_recursive called on non-empty dataframe should return
             the expected non-empty dictionary, but returned {_res0}""",
         )
 
         self.assertSetEqual(
             set(_all_nodes),
             set(_returned_nodes),
-            """compute_mutual_clusters_recursive called on non-empty dataframe should return all nodes""",
+            """get_consensus_partition_recursive called on non-empty dataframe should return all nodes""",
         )
 
     def test_on_three_layers_1(self):
         """
-        compute_mutual_clusters_recursive returns a dictionary, and returns all the nodes
+        get_consensus_partition_recursive returns a dictionary, and returns all the nodes
         """
         _a = pd.DataFrame({"A": [0, 0, 1, 1], "B": [0, 1, 0, 1], "C": [1, 0, 1, 0]})
         _all_nodes = list(_a.index)
-        _res0 = compute_mutual_clusters_recursive(cluster_labels_df=_a)
+        _res0 = get_consensus_partition_recursive(opinions=_a)
         _returned_nodes = [n for mc in _res0.values() for n in mc]
         _expected0 = {
             "A0_B0_C1": {0},
@@ -230,63 +230,63 @@ def test_on_three_layers_1(self):
         self.assertIsInstance(
             _res0,
             dict,
-            f"""compute_mutual_clusters_recursive should return a dictionary, but returned {type(_res0)}""",
+            f"""get_consensus_partition_recursive should return a dictionary, but returned {type(_res0)}""",
         )
 
         self.assertNotEqual(
             _res0,
             dict(),
-            f"""compute_mutual_clusters_recursive called on non-empty dataframe should return a non-empty dictionary,
+            f"""get_consensus_partition_recursive called on non-empty dataframe should return a non-empty dictionary,
             but returned {_res0}""",
         )
 
         self.assertDictEqual(
             _res0,
             _expected0,
-            f"""compute_mutual_clusters_recursive called on non-empty dataframe should return
+            f"""get_consensus_partition_recursive called on non-empty dataframe should return
             the expected non-empty dictionary, but returned {_res0}""",
         )
 
         self.assertSetEqual(
             set(_all_nodes),
             set(_returned_nodes),
-            """compute_mutual_clusters_recursive called on non-empty dataframe should return all nodes""",
+            """get_consensus_partition_recursive called on non-empty dataframe should return all nodes""",
         )
 
     def test_on_three_layers_2(self):
         """
-        compute_mutual_clusters_recursive returns a dictionary, and returns all the nodes
+        get_consensus_partition_recursive returns a dictionary, and returns all the nodes
         """
         _a = pd.DataFrame({"A": [0, 0, 1, 1], "B": [1, 1, 0, 1], "C": [0, 0, 1, 0]})
         _all_nodes = list(_a.index)
-        _res0 = compute_mutual_clusters_recursive(cluster_labels_df=_a)
+        _res0 = get_consensus_partition_recursive(opinions=_a)
         _returned_nodes = [n for mc in _res0.values() for n in mc]
         _expected0 = {"A0_B1_C0": {0, 1}, "A1_B0_C1": {2}, "A1_B1_C0": {3}}
 
         self.assertIsInstance(
             _res0,
             dict,
-            f"""compute_mutual_clusters_recursive should return a dictionary, but returned {type(_res0)}""",
+            f"""get_consensus_partition_recursive should return a dictionary, but returned {type(_res0)}""",
         )
 
         self.assertNotEqual(
             _res0,
             dict(),
-            f"""compute_mutual_clusters_recursive called on non-empty dataframe should return a non-empty dictionary,
+            f"""get_consensus_partition_recursive called on non-empty dataframe should return a non-empty dictionary,
             but returned {_res0}""",
         )
 
         self.assertDictEqual(
             _res0,
             _expected0,
-            f"""compute_mutual_clusters_recursive called on non-empty dataframe should return the expected non-empty
+            f"""get_consensus_partition_recursive called on non-empty dataframe should return the expected non-empty
                 dictionary, but returned {_res0}""",
         )
 
         self.assertSetEqual(
             set(_all_nodes),
             set(_returned_nodes),
-            """compute_mutual_clusters_recursive called on non-empty dataframe should return all nodes""",
+            """get_consensus_partition_recursive called on non-empty dataframe should return all nodes""",
         )